Mercury Edit 2 vs Claude Opus 5 vs Trinity Large Thinking
Trinity Large Thinking comes out ahead, 55 to 37 and 35 on our weighted score.
InceptionMercury Edit 2
35/100- ECI—
- Price$0.25 / $0.75
- Context32K
AnthropicClaude Opus 5
37/100- ECI162.9
- Price$5.00 / $25.00
- Context1M
- Our pick
Arcee AITrinity Large Thinking
55/100- ECI—
- Price$0.25 / $0.80
- Context524K
Trinity Large Thinking is our pick
Trinity Large Thinking is the better all-round choice, scoring 55/100 against Claude Opus 5 (37) and Mercury Edit 2 (35). Claude Opus 5 wins on inputs & features and context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceMercury Edit 2Mercury Edit 2 $0.375 · Trinity Large Thinking $0.388 · Claude Opus 5 $10.00 per 1M tokens (3:1 blend)
- Longest contextClaude Opus 5Claude Opus 5 1,000,000 · Trinity Large Thinking 524,288 · Mercury Edit 2 32,000 tokens
- Widest inputsClaude Opus 5Mercury Edit 2: Text · Claude Opus 5: Text, Images, PDFs · Trinity Large Thinking: Text
- Self-hostingTrinity Large ThinkingPublishes downloadable weights (OpenMDW-1.1)
| Measure | Weight | Mercury Edit 2 | Claude Opus 5 | Trinity Large Thinking |
|---|---|---|---|---|
| Price | 50% | 70 | 2 | 69 |
| Inputs & features | 30% | 0 | 80 | 35 |
| Context window | 20% | 0 | 60 | 49 |
| Overall | 100% | 35/100 | 37/100 | 55/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Mercury Edit 2Inception | Claude Opus 5Anthropic | Trinity Large ThinkingArcee AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 162.9 | — |
| ECI rank | — | #5 of 148 | — |
| GPQA DiamondGraduate-level science questions | — | 93.9% | — |
| FrontierMath Tiers 1–3Research-level mathematics | — | 85.6% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 98.9% | — |
| SimpleQA VerifiedShort factual questions | — | 59.9% | — |
| Price per million tokens | |||
| Input | $0.25 (best) | $5.00 | $0.25 (best) |
| Output | $0.75 (best) | $25.00 | $0.80 |
| Cached input | $0.025 (best) | $0.50 | $0.06 |
| Blended (3:1) | $0.375 (best) | $10.00 | $0.388 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Inception API | Official Anthropic API | Official Arcee API |
| Limits | |||
| Context window | 32,000 tokens | 1,000,000 tokens (best) | 524,288 tokens |
| Max output | 8,192 tokens | 128,000 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | Yes | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | Yeslow · medium · high · xhigh · max | Yes |
| Tool calling | No | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Proprietary | Proprietary | OpenOpenMDW-1.1 |
| API model ID | mercury-edit-2 | claude-opus-5 | trinity-large-thinking |
| API providers | 1 | 35 (best) | 6 |
| Released | Mar 30, 2026 | Jul 24, 2026 | Apr 1, 2026 |
| Knowledge cutoff | — | May 2026 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Mercury Edit 2$4.00
- Claude Opus 5$100.00
- Trinity Large Thinking$4.10
Which should you choose?
Which is better: Mercury Edit 2, Claude Opus 5 or Trinity Large Thinking?
Trinity Large Thinking is the better all-round choice, scoring 55/100 against Claude Opus 5 (37) and Mercury Edit 2 (35). Claude Opus 5 wins on inputs & features and context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Mercury Edit 2, Claude Opus 5 or Trinity Large Thinking?
Mercury Edit 2 is cheaper at $0.25 input / $0.75 output per million tokens (official Inception API price). Trinity Large Thinking costs $0.25 input / $0.80 output per million tokens (official Arcee API price); Claude Opus 5 costs $5.00 input / $25.00 output per million tokens (official Anthropic API price). At a typical mix of three input tokens to one output token, that is $0.375 per million tokens for Mercury Edit 2 versus $0.388 for Trinity Large Thinking (1× as much) and $10.00 for Claude Opus 5 (27× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Mercury Edit 2 has not been scored yet, Claude Opus 5 has an ECI of 162.9 and Trinity Large Thinking has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Mercury Edit 2, Claude Opus 5 and Trinity Large Thinking yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Mercury Edit 2 does not support tool calling, which most coding agents need.
Which has the bigger context window?
Claude Opus 5 has the largest context window at 1,000,000 tokens, against 524,288 for Trinity Large Thinking and 32,000 for Mercury Edit 2. Maximum output per response: Mercury Edit 2 up to 8,192, Claude Opus 5 up to 128,000, Trinity Large Thinking up to 262,144 tokens.
Which can read images, PDFs, audio or video?
Mercury Edit 2 accepts text; Claude Opus 5 accepts text, images and PDFs; Trinity Large Thinking accepts text. Claude Opus 5 handles the widest range of inputs.
Are any of these open source?
Trinity Large Thinking publishes its weights (OpenMDW-1.1) and can be self-hosted; Mercury Edit 2 and Claude Opus 5 is proprietary.
Which is newer?
Claude Opus 5 is the newest, released Jul 24, 2026. Trinity Large Thinking came out Apr 1, 2026; Mercury Edit 2 came out Mar 30, 2026. Knowledge cutoff: Claude Opus 5 May 2026.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
